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Molecular Computational Research in Pharmacological and Toxicological Issues
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Molecular Computational Research in Pharmacological and Toxicological Issues

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".

Deadline for manuscript submissions: closed (20 March 2025) | Viewed by 7094

Special Issue Editor

Prof. Dr. Yoshihiro Uesawa

E-Mail Website
Guest Editor
Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Tokyo 204-858, Japan
Interests: computational toxicology; pharmacokinetics; pharmacovigilance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent improvements in computer hardware, software, and internet infrastructure capabilities have consistently increased the number of analytical research projects that utilize large-scale datasets annually. Various information types, such as those derived from real-world data, high-throughput screening results, and so on, have increasingly become invaluable resources for impactful research. Notable challenges include such areas as drug discovery processes and the rigorous evaluation of drug safety profiles. Consequently, results obtained from analyses based on physicochemical properties of molecules such as drugs and proteins are expected to considerably advance the field of molecular sciences. This impact is anticipated when such results are integrated with traditional experimental methodologies as well as computer-simulated, or in silico, experiments.

This Special Issue is dedicated to the exploration of database analysis and in silico methodologies in the fields of drug discovery, clinical research, and regulatory science. It will feature articles that delve into computer-simulated approaches for drug design and discovery, the incorporation of AI technologies in protein engineering, and in silico models for predicting the toxicity of drugs and chemicals.

Prof. Dr. Yoshihiro Uesawa
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

  • in silico study
  • computational toxicology
  • adverse drug reaction
  • drug discovery
  • QSAR analysis
  • machine learning
  • artificial intelligence

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Published Papers (3 papers)

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Research

18 pages, 4093 KiB  
Article
PTB-DDI: An Accurate and Simple Framework for Drug–Drug Interaction Prediction Based on Pre-Trained Tokenizer and BiLSTM Model
by Jiayue Qiu, Xiao Yan, Yanan Tian, Qin Li, Xiaomeng Liu, Yuwei Yang, Henry H. Y. Tong and Huanxiang Liu
Int. J. Mol. Sci. 2024, 25(21), 11385; https://doi.org/10.3390/ijms252111385 - 23 Oct 2024
Viewed by 1500
Abstract
The simultaneous use of two or more drugs in clinical treatment may raise the risk of a drug–drug interaction (DDI). DDI prediction is very important to avoid adverse drug events in combination therapy. Recently, deep learning methods have been applied successfully to DDI [...] Read more.
The simultaneous use of two or more drugs in clinical treatment may raise the risk of a drug–drug interaction (DDI). DDI prediction is very important to avoid adverse drug events in combination therapy. Recently, deep learning methods have been applied successfully to DDI prediction and improved prediction performance. However, there are still some problems with the present models, such as low accuracy due to information loss during molecular representation or incomplete drug feature mining during the training process. Aiming at these problems, this study proposes an accurate and simple framework named PTB-DDI for drug–drug interaction prediction. The PTB-DDI framework consists of four key modules: (1) ChemBerta tokenizer for molecular representation, (2) Bidirectional Long Short-Term Memory (BiLSTM) to capture the bidirectional context-aware features of drugs, (3) Multilayer Perceptron (MLP) for mining the nonlinear relationship of drug features, and (4) interaction predictor to perform an affine transformation and final prediction. In addition, we investigate the effect of dual-mode on parameter-sharing and parameter-independent within the PTB-DDI framework. Furthermore, we conducted comprehensive experiments on the two real-world datasets (i.e., BIOSNAP and DrugBank) to evaluate PTB-DDI framework performance. The results show that our proposed framework has significant improvements over the baselines based on both datasets. Based on the BIOSNAP dataset, the AUC-ROC, PR-AUC, and F1 scores are 0.997, 0.995, and 0.984, respectively. These metrics are 0.896, 0.873, and 0.826 based on the DrugBank dataset. Then, we conduct the case studies on the three newly approved drugs by the Food and Drug Administration (FDA) in 2024 using the PTB-DDI framework in dual modes. The obtained results indicate that our proposed framework has advantages for predicting drug–drug interactions and that the dual modes of the framework complement each other. Furthermore, a free website is developed to enhance accessibility and user experience. Full article
(This article belongs to the Special Issue Molecular Computational Research in Pharmacological and Toxicological Issues)
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14 pages, 1783 KiB  
Article
Progress in Predicting Ames Test Outcomes from Chemical Structures: An In-Depth Re-Evaluation of Models from the 1st and 2nd Ames/QSAR International Challenge Projects
by Yoshihiro Uesawa
Int. J. Mol. Sci. 2024, 25(3), 1373; https://doi.org/10.3390/ijms25031373 - 23 Jan 2024
Cited by 4 | Viewed by 1985
Abstract
The Ames/quantitative structure–activity relationship (QSAR) International Challenge Projects, held during 2014–2017 and 2020–2022, evaluated the performance of various predictive models. Despite the significant insights gained, the rules allowing participants to select prediction targets introduced ambiguity in model performance evaluation. This reanalysis identified the [...] Read more.
The Ames/quantitative structure–activity relationship (QSAR) International Challenge Projects, held during 2014–2017 and 2020–2022, evaluated the performance of various predictive models. Despite the significant insights gained, the rules allowing participants to select prediction targets introduced ambiguity in model performance evaluation. This reanalysis identified the highest-performing prediction model, assuming a 100% coverage rate (COV) for all prediction target compounds and an estimated performance variation due to changes in COV. All models from both projects were evaluated using balance accuracy (BA), the Matthews correlation coefficient (MCC), the F1 score (F1), and the first principal component (PC1). After normalizing the COV, a correlation analysis with these indicators was conducted, and the evaluation index for all prediction models in terms of the COV was estimated. In total, using 109 models, the model with the highest estimated BA (76.9) at 100% COV was MMI-VOTE1, as reported by Meiji Pharmaceutical University (MPU). The best models for MCC, F1, and PC1 were all MMI-STK1, also reported by MPU. All the models reported by MPU ranked in the top four. MMI-STK1 was estimated to have F1 scores of 59.2, 61.5, and 63.1 at COV levels of 90%, 60%, and 30%, respectively. These findings highlight the current state and potential of the Ames prediction technology. Full article
(This article belongs to the Special Issue Molecular Computational Research in Pharmacological and Toxicological Issues)
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22 pages, 14481 KiB  
Article
In Vivo and In Silico Studies of the Hepatoprotective Activity of Tert-Butylhydroquinone
by Liseth Rubi Aldaba-Muruato, Sandra Sánchez-Barbosa, Víctor Hugo Rodríguez-Purata, Georgina Cabrera-Cruz, Estefany Rosales-Domínguez, Daniela Martínez-Valentín, Yoshio Aldo Alarcón-López, Pablo Aguirre-Vidal, Manuel Alejandro Hernández-Serda, Luis Alfonso Cárdenas-Granados, Víctor Hugo Vázquez-Valadez, Enrique Angeles and José Roberto Macías-Pérez
Int. J. Mol. Sci. 2024, 25(1), 475; https://doi.org/10.3390/ijms25010475 - 29 Dec 2023
Cited by 2 | Viewed by 3013
Abstract
Tert-butylhydroquinone (TBHQ) is a synthetic food antioxidant with biological activities, but little is known about its pharmacological benefits in liver disease. Therefore, this work aimed to evaluate TBHQ during acute liver damage induced by CCl4 (24 h) or BDL (48 h) [...] Read more.
Tert-butylhydroquinone (TBHQ) is a synthetic food antioxidant with biological activities, but little is known about its pharmacological benefits in liver disease. Therefore, this work aimed to evaluate TBHQ during acute liver damage induced by CCl4 (24 h) or BDL (48 h) in Wistar rats. It was found that pretreatment with TBHQ prevents 50% of mortality induced by a lethal dose of CCl4 (4 g/kg, i.p.), and 80% of BDL+TBHQ rats survived, while only 50% of the BDL group survived. Serum markers of liver damage and macroscopic and microscopic (H&E staining) observations suggest that TBHQ protects from both hepatocellular necrosis caused by the sublethal dose of CCl4 (1.6 g/kg, i.p.), as well as necrosis/ductal proliferation caused by BDL. Additionally, online databases identified 49 potential protein targets for TBHQ. Finally, a biological target candidate (Keap1) was evaluated in a proof-of-concept in silico molecular docking assay, resulting in an interaction energy of −5.5491 kcal/mol, which was higher than RA839 and lower than monoethyl fumarate (compounds known to bind to Keap1). These findings suggest that TBHQ increases the survival of animals subjected to CCl4 intoxication or BDL, presumably by reducing hepatocellular damage, probably due to the interaction of TBHQ with Keap1. Full article
(This article belongs to the Special Issue Molecular Computational Research in Pharmacological and Toxicological Issues)
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